Article in Skeletal muscle, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 2 of them syntheses that pooled it.
0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 2 pooled it
1.0field-weighted citation impact, top 22% of its field
1 · What the graph read from it
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
2 · The registry
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
3 · Its place in the literature
Who cites it
6 citing papers in PubMed, 2 syntheses or guidelines pooled it, 11 citations in OpenAlex.
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
5 · Who and what money
Authors and funding
10 authors at 3 institutions in 2 countries.
Yu-Fang PeiDepartment of Epidemiology and Health Statistics, School of Public Health, Medical College, SuZhou City, People's Republic of China.
Wen-Zhu HuSchool of Public Health, Southeast University, Nanjing, People's Republic of China.
Xiao-Lin YangJiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Soochow University, SuZhou City, People's Republic of China.
Xin-Tong WeiDepartment of Epidemiology and Health Statistics, School of Public Health, Medical College, SuZhou City, People's Republic of China.
Gui-Juan FengDepartment of Epidemiology and Health Statistics, School of Public Health, Medical College, SuZhou City, People's Republic of China.
Hong ZhangJiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Soochow University, SuZhou City, People's Republic of China.
Hui ShenDepartment of Biostatistics and Bioinformatics, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, USA.
Qing TianDepartment of Biostatistics and Bioinformatics, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, USA.
Hong-Wen DengDepartment of Biostatistics and Bioinformatics, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, USA.
Lei ZhangJiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Soochow University, SuZhou City, People's Republic of China. lzhang6@suda.edu.cn.ORCID 0000-0002-1007-4834
Soochow University · CNTulane University · USSoutheast University · CN
Funding
Tulane COBRE in Cardiometabolic Diseases Clinical Research CoreP20GM109036 · NIGMS · TULANE UNIVERSITY OF LOUISIANA · PI Katherine Teresa Mills · 2016 to 2026
$25.3M
Trans-omics Integration of Multi-omics Studies for OsteoporosisU19AG055373 · NIA · TULANE UNIVERSITY OF LOUISIANA · PI Chuan Qiu · 2017 to 2026
$24.3M
RISK FACTORS FOR VERTEBRAL FRACTURE AND BONE LOSSR01AR041398 · NIAMS · RHODE ISLAND HOSPITAL (PROVIDENCE, RI) · PI KIEL, DOUGLAS P. · 1991 to 2025
$16.1M
Multivariate methods for identifying multitask/multimodal brain imaging biomarkersR01EB006841 · NIBIB · THE MIND RESEARCH NETWORK · PI VINCE D CALHOUN, Sergey Plis · 2007 to 2026
$9.8M
Epigenomewide DNA Methylation Study for Osteoporosis RiskR01AR059781 · NIAMS · TULANE UNIVERSITY OF LOUISIANA · PI DENG, HONG-WEN · 2012 to 2016
$3.1M
Decoding Methylation Mediated Epigenomic Contributions to Male OsteoporosisR01AR069055 · NIAMS · TULANE UNIVERSITY OF LOUISIANA · PI DENG, HONG-WEN · 2017 to 2021
$2.9M
Unified multivariate data-driven solutions for static and dynamic brain connectivityR01EB020407 · NIBIB · THE MIND RESEARCH NETWORK · PI ADALI, TULAY, CALHOUN, VINCE D · 2015 to 2018
$2.7M
Integration of brain imaging with genomic and epigenomic dataR01MH104680 · NIMH · TULANE UNIVERSITY OF LOUISIANA · PI CALHOUN, VINCE D, DENG, HONG-WEN · 2014 to 2017
$2.1M
WOMENS HEALTH INITIATIVE-N01WH22110N01WH022110 · WH · FRED HUTCHINSON CANCER RESEARCH CENTER · PI ROSS, PRENTICE · 1992 to 2006
$1.2M
SLEEP AND ENTRAINMENT OF SCN FUNCTIONR01HL064278 · NHLBI · CASE WESTERN RESERVE UNIVERSITY · PI STROHL, KINGMAN PERKINS · 1999 to 2002
$871k
THE FRAMINGHAM HEART STUDY-N01HC25195-268025195-268025195N01HC025195 · HC · TRUSTEES OF BOSTON UNIVERSITY · PI WOLF, PHILIP A · 2002 to 2006
backgroundLow lean body mass is the most important predictor of sarcopenia with strong genetic background. The aim of this study was to uncover genetic factors underlying lean mass development. MATERIALS AND
methodsWe performed a genome-wide association study (GWAS) of fat-adjusted leg lean mass in the Framingham Heart Study (FHS, N = 6587), and replicated in the Women's Health Initiative-African American sub-sample (WHI-AA, N = 847) and the Kansas City Osteoporosis Study (KCOS, N = 2219). We also cross-validated significant variants in the publicly available body mass index (BMI) summary results (N ~ 700,000). We then performed a series of functional investigations on the identified variants.
resultsFour correlated SNPs at 6p21.1 were identified at the genome-wide significance (GWS, α = 5.0 × 10
conclusionsThrough a large-scale GWAS followed by a series of functional investigations, we identified 2 correlated functional variants at 6p21.1 associated with leg lean mass. Our findings not only enhanced our understanding of molecular basis of lean mass development but also provided useful candidate genes for further functional studies.
Indexed as
Polymorphism, Single NucleotideAgedAnimalsBody Mass IndexCell LineChromosomes, Human, Pair 6FemaleGenetic Predisposition to DiseaseGenome-Wide Association StudyHumansI-kappa B ProteinsLinkage DisequilibriumMaleMiceMiddle AgedProto-Oncogene ProteinsI-kappa B ProteinsNFKBIE protein, humanProto-Oncogene Proteins6p21.1Genome-wide association studyLean body massNFKBIE
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.
Two functional variants at 6p21.1 were associated with lean mass. · full record | OpenQuestion